Overview

Dataset statistics

Number of variables34
Number of observations142588
Missing cells781636
Missing cells (%)16.1%
Duplicate rows33
Duplicate rows (%)< 0.1%
Total size in memory37.0 MiB
Average record size in memory272.0 B

Variable types

Numeric7
Boolean4
Categorical12
DateTime4
Text6
Unsupported1

Alerts

Dataset has 33 (< 0.1%) duplicate rowsDuplicates
is highly overall correlated with business_name and 1 other fieldsHigh correlation
business_name is highly overall correlated with and 2 other fieldsHigh correlation
case_linked is highly overall correlated with business_name and 1 other fieldsHigh correlation
case_type is highly overall correlated with case_linkedHigh correlation
customer_type is highly overall correlated with High correlation
day_of_month is highly overall correlated with month_periodHigh correlation
month_period is highly overall correlated with day_of_monthHigh correlation
referred is highly overall correlated with business_nameHigh correlation
total_cxl_appointments_before_appointment is highly overall correlated with total_good_appointments_before_appointmentHigh correlation
total_good_appointments_before_appointment is highly overall correlated with total_cxl_appointments_before_appointmentHigh correlation
patient_status is highly imbalanced (81.3%)Imbalance
patient_type is highly imbalanced (62.1%)Imbalance
missed is highly imbalanced (89.4%)Imbalance
title is highly imbalanced (54.8%)Imbalance
sex is highly imbalanced (63.6%)Imbalance
case_type has 61949 (43.4%) missing valuesMissing
next_appointment_time has 116141 (81.5%) missing valuesMissing
cancelled_at has 119696 (83.9%) missing valuesMissing
billable_item has 3942 (2.8%) missing valuesMissing
category has 24476 (17.2%) missing valuesMissing
customer_type has 103758 (72.8%) missing valuesMissing
title has 58854 (41.3%) missing valuesMissing
state has 71910 (50.4%) missing valuesMissing
date_of_birth has 2578 (1.8%) missing valuesMissing
sex has 49112 (34.4%) missing valuesMissing
post_code has 51112 (35.8%) missing valuesMissing
city has 49994 (35.1%) missing valuesMissing
occupation has 68082 (47.7%) missing valuesMissing
post_code is an unsupported type, check if it needs cleaning or further analysisUnsupported
total_open_invoices_before_appointemnt has 133150 (93.4%) zerosZeros
total_good_appointments_before_appointment has 15297 (10.7%) zerosZeros
total_cxl_appointments_before_appointment has 87748 (61.5%) zerosZeros
notice has 119702 (83.9%) zerosZeros

Reproduction

Analysis started2024-06-09 11:06:47.030382
Analysis finished2024-06-09 11:07:48.681626
Duration1 minute and 1.65 second
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables


Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.531125
Minimum2
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-06-09T16:07:48.772180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12
Q112
median13
Q316
95-th percentile16
Maximum16
Range14
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.8397643
Coefficient of variation (CV)0.13596536
Kurtosis-1.4498233
Mean13.531125
Median Absolute Deviation (MAD)1
Skewness0.45622436
Sum1929376
Variance3.3847326
MonotonicityNot monotonic
2024-06-09T16:07:48.868159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
12 57664
40.4%
16 48654
34.1%
13 29681
20.8%
11 6410
 
4.5%
15 171
 
0.1%
2 8
 
< 0.1%
ValueCountFrequency (%)
2 8
 
< 0.1%
11 6410
 
4.5%
12 57664
40.4%
13 29681
20.8%
15 171
 
0.1%
16 48654
34.1%
ValueCountFrequency (%)
16 48654
34.1%
15 171
 
0.1%
13 29681
20.8%
12 57664
40.4%
11 6410
 
4.5%
2 8
 
< 0.1%

case_linked
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size139.4 KiB
True
80639 
False
61949 
ValueCountFrequency (%)
True 80639
56.6%
False 61949
43.4%
2024-06-09T16:07:48.965021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

case_type
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing61949
Missing (%)43.4%
Memory size1.1 MiB
Unlimited
59368 
Max Sessions
21167 
Max Amount
 
104

Length

Max length12
Median length9
Mean length9.7887623
Min length9

Characters and Unicode

Total characters789356
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnlimited
2nd rowUnlimited
3rd rowUnlimited
4th rowUnlimited
5th rowUnlimited

Common Values

ValueCountFrequency (%)
Unlimited 59368
41.6%
Max Sessions 21167
 
14.8%
Max Amount 104
 
0.1%
(Missing) 61949
43.4%

Length

2024-06-09T16:07:49.060284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-09T16:07:49.163681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
unlimited 59368
58.3%
max 21271
 
20.9%
sessions 21167
 
20.8%
amount 104
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i 139903
17.7%
n 80639
10.2%
e 80535
10.2%
s 63501
8.0%
m 59472
7.5%
t 59472
7.5%
U 59368
7.5%
l 59368
7.5%
d 59368
7.5%
21271
 
2.7%
Other values (7) 106459
13.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 789356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 139903
17.7%
n 80639
10.2%
e 80535
10.2%
s 63501
8.0%
m 59472
7.5%
t 59472
7.5%
U 59368
7.5%
l 59368
7.5%
d 59368
7.5%
21271
 
2.7%
Other values (7) 106459
13.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 789356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 139903
17.7%
n 80639
10.2%
e 80535
10.2%
s 63501
8.0%
m 59472
7.5%
t 59472
7.5%
U 59368
7.5%
l 59368
7.5%
d 59368
7.5%
21271
 
2.7%
Other values (7) 106459
13.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 789356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 139903
17.7%
n 80639
10.2%
e 80535
10.2%
s 63501
8.0%
m 59472
7.5%
t 59472
7.5%
U 59368
7.5%
l 59368
7.5%
d 59368
7.5%
21271
 
2.7%
Other values (7) 106459
13.5%
Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15844251
Minimum0
Maximum36
Zeros133150
Zeros (%)93.4%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-06-09T16:07:49.263007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum36
Range36
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.89940366
Coefficient of variation (CV)5.6765302
Kurtosis236.06917
Mean0.15844251
Median Absolute Deviation (MAD)0
Skewness12.036222
Sum22592
Variance0.80892695
MonotonicityNot monotonic
2024-06-09T16:07:49.379022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 133150
93.4%
1 4897
 
3.4%
2 1966
 
1.4%
3 933
 
0.7%
4 597
 
0.4%
5 267
 
0.2%
6 179
 
0.1%
7 159
 
0.1%
9 120
 
0.1%
8 92
 
0.1%
Other values (27) 228
 
0.2%
ValueCountFrequency (%)
0 133150
93.4%
1 4897
 
3.4%
2 1966
 
1.4%
3 933
 
0.7%
4 597
 
0.4%
5 267
 
0.2%
6 179
 
0.1%
7 159
 
0.1%
8 92
 
0.1%
9 120
 
0.1%
ValueCountFrequency (%)
36 1
< 0.1%
35 1
< 0.1%
34 1
< 0.1%
33 1
< 0.1%
32 1
< 0.1%
31 1
< 0.1%
30 1
< 0.1%
29 1
< 0.1%
28 1
< 0.1%
27 2
< 0.1%

total_good_appointments_before_appointment
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct335
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.58918
Minimum0
Maximum334
Zeros15297
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-06-09T16:07:49.495181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q310
95-th percentile45
Maximum334
Range334
Interquartile range (IQR)9

Descriptive statistics

Standard deviation23.449078
Coefficient of variation (CV)2.2144375
Kurtosis44.197321
Mean10.58918
Median Absolute Deviation (MAD)2
Skewness5.7101934
Sum1509890
Variance549.85924
MonotonicityNot monotonic
2024-06-09T16:07:49.710981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 32620
22.9%
0 15297
10.7%
2 13329
 
9.3%
3 10798
 
7.6%
4 8875
 
6.2%
5 7313
 
5.1%
6 5940
 
4.2%
7 4888
 
3.4%
8 4146
 
2.9%
9 3541
 
2.5%
Other values (325) 35841
25.1%
ValueCountFrequency (%)
0 15297
10.7%
1 32620
22.9%
2 13329
9.3%
3 10798
 
7.6%
4 8875
 
6.2%
5 7313
 
5.1%
6 5940
 
4.2%
7 4888
 
3.4%
8 4146
 
2.9%
9 3541
 
2.5%
ValueCountFrequency (%)
334 1
< 0.1%
333 1
< 0.1%
332 1
< 0.1%
331 1
< 0.1%
330 1
< 0.1%
329 1
< 0.1%
328 1
< 0.1%
327 1
< 0.1%
326 1
< 0.1%
325 1
< 0.1%

total_cxl_appointments_before_appointment
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct134
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6344924
Minimum0
Maximum145
Zeros87748
Zeros (%)61.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-06-09T16:07:49.831964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile13
Maximum145
Range145
Interquartile range (IQR)2

Descriptive statistics

Standard deviation8.6717357
Coefficient of variation (CV)3.2916154
Kurtosis76.642096
Mean2.6344924
Median Absolute Deviation (MAD)0
Skewness7.4385572
Sum375647
Variance75.199
MonotonicityNot monotonic
2024-06-09T16:07:49.971852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 87748
61.5%
1 19022
 
13.3%
2 8624
 
6.0%
3 5195
 
3.6%
4 3739
 
2.6%
5 2731
 
1.9%
6 2069
 
1.5%
7 1472
 
1.0%
8 1220
 
0.9%
9 1155
 
0.8%
Other values (124) 9613
 
6.7%
ValueCountFrequency (%)
0 87748
61.5%
1 19022
 
13.3%
2 8624
 
6.0%
3 5195
 
3.6%
4 3739
 
2.6%
5 2731
 
1.9%
6 2069
 
1.5%
7 1472
 
1.0%
8 1220
 
0.9%
9 1155
 
0.8%
ValueCountFrequency (%)
145 22
< 0.1%
144 12
< 0.1%
143 22
< 0.1%
142 1
 
< 0.1%
141 2
 
< 0.1%
140 3
 
< 0.1%
139 2
 
< 0.1%
138 3
 
< 0.1%
137 1
 
< 0.1%
136 3
 
< 0.1%

patient_status
Categorical

IMBALANCE 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Not Yet Actioned
129550 
Therapist Follow Up
 
7366
Has An Upcoming Booking
 
2191
Patient Stopped Treatment
 
1694
Admin Follow Up
 
850
Other values (5)
 
937

Length

Max length25
Median length16
Mean length16.380516
Min length14

Characters and Unicode

Total characters2335665
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Yet Actioned
2nd rowNot Yet Actioned
3rd rowNot Yet Actioned
4th rowNot Yet Actioned
5th rowNot Yet Actioned

Common Values

ValueCountFrequency (%)
Not Yet Actioned 129550
90.9%
Therapist Follow Up 7366
 
5.2%
Has An Upcoming Booking 2191
 
1.5%
Patient Stopped Treatment 1694
 
1.2%
Admin Follow Up 850
 
0.6%
Discharged By Therapist 352
 
0.2%
On Waiting List 322
 
0.2%
Staff & Family 126
 
0.1%
Contacted Too Many Times 81
 
0.1%
Do Not Contact 56
 
< 0.1%

Length

2024-06-09T16:07:50.096019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-09T16:07:50.198563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
not 129606
30.1%
actioned 129550
30.1%
yet 129550
30.1%
follow 8216
 
1.9%
up 8216
 
1.9%
therapist 7718
 
1.8%
has 2191
 
0.5%
an 2191
 
0.5%
upcoming 2191
 
0.5%
booking 2191
 
0.5%
Other values (18) 8416
 
2.0%

Most occurring characters

ValueCountFrequency (%)
t 405938
17.4%
287448
12.3%
o 284210
12.2%
e 274108
11.7%
i 145719
 
6.2%
n 141223
 
6.0%
A 132591
 
5.7%
d 132527
 
5.7%
c 132230
 
5.7%
N 129606
 
5.5%
Other values (27) 270065
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2335665
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 405938
17.4%
287448
12.3%
o 284210
12.2%
e 274108
11.7%
i 145719
 
6.2%
n 141223
 
6.0%
A 132591
 
5.7%
d 132527
 
5.7%
c 132230
 
5.7%
N 129606
 
5.5%
Other values (27) 270065
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2335665
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 405938
17.4%
287448
12.3%
o 284210
12.2%
e 274108
11.7%
i 145719
 
6.2%
n 141223
 
6.0%
A 132591
 
5.7%
d 132527
 
5.7%
c 132230
 
5.7%
N 129606
 
5.5%
Other values (27) 270065
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2335665
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 405938
17.4%
287448
12.3%
o 284210
12.2%
e 274108
11.7%
i 145719
 
6.2%
n 141223
 
6.0%
A 132591
 
5.7%
d 132527
 
5.7%
c 132230
 
5.7%
N 129606
 
5.5%
Other values (27) 270065
11.6%

patient_type
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Recurring
119797 
new injury/issue
12173 
New To Clinic
 
6249
new to therapist
 
3908
new to service
 
461

Length

Max length16
Median length9
Mean length9.9809241
Min length9

Characters and Unicode

Total characters1423160
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew To Clinic
2nd rowRecurring
3rd rowRecurring
4th rowRecurring
5th rowNew To Clinic

Common Values

ValueCountFrequency (%)
Recurring 119797
84.0%
new injury/issue 12173
 
8.5%
New To Clinic 6249
 
4.4%
new to therapist 3908
 
2.7%
new to service 461
 
0.3%

Length

2024-06-09T16:07:50.346217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-09T16:07:50.443209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
recurring 119797
68.1%
new 22791
 
12.9%
injury/issue 12173
 
6.9%
to 10618
 
6.0%
clinic 6249
 
3.6%
therapist 3908
 
2.2%
service 461
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r 256136
18.0%
i 161010
11.3%
e 159591
11.2%
n 154761
10.9%
u 144143
10.1%
c 126507
8.9%
R 119797
8.4%
g 119797
8.4%
33409
 
2.3%
s 28715
 
2.0%
Other values (14) 119294
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1423160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 256136
18.0%
i 161010
11.3%
e 159591
11.2%
n 154761
10.9%
u 144143
10.1%
c 126507
8.9%
R 119797
8.4%
g 119797
8.4%
33409
 
2.3%
s 28715
 
2.0%
Other values (14) 119294
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1423160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 256136
18.0%
i 161010
11.3%
e 159591
11.2%
n 154761
10.9%
u 144143
10.1%
c 126507
8.9%
R 119797
8.4%
g 119797
8.4%
33409
 
2.3%
s 28715
 
2.0%
Other values (14) 119294
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1423160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 256136
18.0%
i 161010
11.3%
e 159591
11.2%
n 154761
10.9%
u 144143
10.1%
c 126507
8.9%
R 119797
8.4%
g 119797
8.4%
33409
 
2.3%
s 28715
 
2.0%
Other values (14) 119294
8.4%

time_of_day
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Late Afternoon
68696 
Late Morning
50404 
Early Morning
14476 
Evening
9012 

Length

Max length14
Median length13
Mean length12.749067
Min length7

Characters and Unicode

Total characters1817864
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEarly Morning
2nd rowLate Afternoon
3rd rowLate Morning
4th rowLate Morning
5th rowEarly Morning

Common Values

ValueCountFrequency (%)
Late Afternoon 68696
48.2%
Late Morning 50404
35.3%
Early Morning 14476
 
10.2%
Evening 9012
 
6.3%

Length

2024-06-09T16:07:50.563884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-09T16:07:50.659265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
late 119100
43.1%
afternoon 68696
24.9%
morning 64880
23.5%
early 14476
 
5.2%
evening 9012
 
3.3%

Most occurring characters

ValueCountFrequency (%)
n 285176
15.7%
o 202272
11.1%
e 196808
10.8%
t 187796
10.3%
r 148052
8.1%
133576
7.3%
a 133576
7.3%
L 119100
6.6%
i 73892
 
4.1%
g 73892
 
4.1%
Other values (7) 263724
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1817864
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 285176
15.7%
o 202272
11.1%
e 196808
10.8%
t 187796
10.3%
r 148052
8.1%
133576
7.3%
a 133576
7.3%
L 119100
6.6%
i 73892
 
4.1%
g 73892
 
4.1%
Other values (7) 263724
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1817864
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 285176
15.7%
o 202272
11.1%
e 196808
10.8%
t 187796
10.3%
r 148052
8.1%
133576
7.3%
a 133576
7.3%
L 119100
6.6%
i 73892
 
4.1%
g 73892
 
4.1%
Other values (7) 263724
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1817864
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 285176
15.7%
o 202272
11.1%
e 196808
10.8%
t 187796
10.3%
r 148052
8.1%
133576
7.3%
a 133576
7.3%
L 119100
6.6%
i 73892
 
4.1%
g 73892
 
4.1%
Other values (7) 263724
14.5%

month_period
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Mid month
49458 
End month
46869 
Early month
46261 

Length

Max length11
Median length9
Mean length9.6488765
Min length9

Characters and Unicode

Total characters1375814
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMid month
2nd rowMid month
3rd rowMid month
4th rowMid month
5th rowEnd month

Common Values

ValueCountFrequency (%)
Mid month 49458
34.7%
End month 46869
32.9%
Early month 46261
32.4%

Length

2024-06-09T16:07:50.780707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-09T16:07:50.883167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
month 142588
50.0%
mid 49458
 
17.3%
end 46869
 
16.4%
early 46261
 
16.2%

Most occurring characters

ValueCountFrequency (%)
n 189457
13.8%
142588
10.4%
m 142588
10.4%
o 142588
10.4%
t 142588
10.4%
h 142588
10.4%
d 96327
7.0%
E 93130
6.8%
M 49458
 
3.6%
i 49458
 
3.6%
Other values (4) 185044
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1375814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 189457
13.8%
142588
10.4%
m 142588
10.4%
o 142588
10.4%
t 142588
10.4%
h 142588
10.4%
d 96327
7.0%
E 93130
6.8%
M 49458
 
3.6%
i 49458
 
3.6%
Other values (4) 185044
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1375814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 189457
13.8%
142588
10.4%
m 142588
10.4%
o 142588
10.4%
t 142588
10.4%
h 142588
10.4%
d 96327
7.0%
E 93130
6.8%
M 49458
 
3.6%
i 49458
 
3.6%
Other values (4) 185044
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1375814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 189457
13.8%
142588
10.4%
m 142588
10.4%
o 142588
10.4%
t 142588
10.4%
h 142588
10.4%
d 96327
7.0%
E 93130
6.8%
M 49458
 
3.6%
i 49458
 
3.6%
Other values (4) 185044
13.4%

day_of_week
Categorical

Distinct7
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Memory size1.1 MiB
Thursday
29586 
Tuesday
27986 
Wednesday
27877 
Monday
24398 
Friday
24219 
Other values (2)
8514 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1283220
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFriday
2nd rowThursday
3rd rowThursday
4th rowFriday
5th rowMonday

Common Values

ValueCountFrequency (%)
Thursday 29586
20.7%
Tuesday 27986
19.6%
Wednesday 27877
19.6%
Monday 24398
17.1%
Friday 24219
17.0%
Saturday 7958
 
5.6%
Sunday 556
 
0.4%
(Missing) 8
 
< 0.1%

Length

2024-06-09T16:07:50.975651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-09T16:07:51.081436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
thursday 29586
20.8%
tuesday 27986
19.6%
wednesday 27877
19.6%
monday 24398
17.1%
friday 24219
17.0%
saturday 7958
 
5.6%
sunday 556
 
0.4%

Most occurring characters

ValueCountFrequency (%)
241035
18.8%
d 170457
13.3%
a 150538
11.7%
y 142580
11.1%
s 85449
 
6.7%
e 83740
 
6.5%
u 66086
 
5.2%
r 61763
 
4.8%
T 57572
 
4.5%
n 52831
 
4.1%
Other values (8) 171169
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1283220
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
241035
18.8%
d 170457
13.3%
a 150538
11.7%
y 142580
11.1%
s 85449
 
6.7%
e 83740
 
6.5%
u 66086
 
5.2%
r 61763
 
4.8%
T 57572
 
4.5%
n 52831
 
4.1%
Other values (8) 171169
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1283220
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
241035
18.8%
d 170457
13.3%
a 150538
11.7%
y 142580
11.1%
s 85449
 
6.7%
e 83740
 
6.5%
u 66086
 
5.2%
r 61763
 
4.8%
T 57572
 
4.5%
n 52831
 
4.1%
Other values (8) 171169
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1283220
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
241035
18.8%
d 170457
13.3%
a 150538
11.7%
y 142580
11.1%
s 85449
 
6.7%
e 83740
 
6.5%
u 66086
 
5.2%
r 61763
 
4.8%
T 57572
 
4.5%
n 52831
 
4.1%
Other values (8) 171169
13.3%

month_of_year
Categorical

Distinct12
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Memory size1.1 MiB
May
15437 
March
14400 
February
13293 
June
13225 
April
12850 
Other values (7)
73375 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1283220
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJuly
2nd rowAugust
3rd rowAugust
4th rowSeptember
5th rowSeptember

Common Values

ValueCountFrequency (%)
May 15437
10.8%
March 14400
10.1%
February 13293
9.3%
June 13225
9.3%
April 12850
9.0%
November 12360
8.7%
January 12343
8.7%
December 10663
7.5%
July 9976
7.0%
October 9622
6.7%
Other values (2) 18411
12.9%

Length

2024-06-09T16:07:51.199455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
may 15437
10.8%
march 14400
10.1%
february 13293
9.3%
june 13225
9.3%
april 12850
9.0%
november 12360
8.7%
january 12343
8.7%
december 10663
7.5%
july 9976
7.0%
october 9622
6.7%
Other values (2) 18411
12.9%

Most occurring characters

ValueCountFrequency (%)
426589
33.2%
e 119366
 
9.3%
r 107663
 
8.4%
u 67981
 
5.3%
a 67816
 
5.3%
b 54777
 
4.3%
y 51049
 
4.0%
J 35544
 
2.8%
c 34685
 
2.7%
m 31862
 
2.5%
Other values (17) 285888
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1283220
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
426589
33.2%
e 119366
 
9.3%
r 107663
 
8.4%
u 67981
 
5.3%
a 67816
 
5.3%
b 54777
 
4.3%
y 51049
 
4.0%
J 35544
 
2.8%
c 34685
 
2.7%
m 31862
 
2.5%
Other values (17) 285888
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1283220
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
426589
33.2%
e 119366
 
9.3%
r 107663
 
8.4%
u 67981
 
5.3%
a 67816
 
5.3%
b 54777
 
4.3%
y 51049
 
4.0%
J 35544
 
2.8%
c 34685
 
2.7%
m 31862
 
2.5%
Other values (17) 285888
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1283220
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
426589
33.2%
e 119366
 
9.3%
r 107663
 
8.4%
u 67981
 
5.3%
a 67816
 
5.3%
b 54777
 
4.3%
y 51049
 
4.0%
J 35544
 
2.8%
c 34685
 
2.7%
m 31862
 
2.5%
Other values (17) 285888
22.3%

day_of_month
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15.621911
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-06-09T16:07:51.292498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6304372
Coefficient of variation (CV)0.55245722
Kurtosis-1.1425088
Mean15.621911
Median Absolute Deviation (MAD)7
Skewness0.031020007
Sum2227372
Variance74.484447
MonotonicityNot monotonic
2024-06-09T16:07:51.398713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
15 5282
 
3.7%
20 5142
 
3.6%
13 5108
 
3.6%
22 5045
 
3.5%
8 5008
 
3.5%
16 4960
 
3.5%
14 4955
 
3.5%
9 4886
 
3.4%
19 4868
 
3.4%
11 4834
 
3.4%
Other values (21) 92492
64.9%
ValueCountFrequency (%)
1 4287
3.0%
2 4307
3.0%
3 4529
3.2%
4 4740
3.3%
5 4682
3.3%
6 4820
3.4%
7 4634
3.2%
8 5008
3.5%
9 4886
3.4%
10 4368
3.1%
ValueCountFrequency (%)
31 2597
1.8%
30 3998
2.8%
29 4335
3.0%
28 4434
3.1%
27 4715
3.3%
26 3792
2.7%
25 3844
2.7%
24 4466
3.1%
23 4811
3.4%
22 5045
3.5%

week_of_year
Real number (ℝ)

Distinct53
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean24.938708
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-06-09T16:07:51.512193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q112
median23
Q338
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation14.939851
Coefficient of variation (CV)0.59906273
Kurtosis-1.1778436
Mean24.938708
Median Absolute Deviation (MAD)13
Skewness0.20220916
Sum3555761
Variance223.19913
MonotonicityNot monotonic
2024-06-09T16:07:51.649871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 3688
 
2.6%
12 3505
 
2.5%
20 3477
 
2.4%
19 3431
 
2.4%
23 3419
 
2.4%
18 3366
 
2.4%
10 3365
 
2.4%
21 3359
 
2.4%
6 3287
 
2.3%
8 3257
 
2.3%
Other values (43) 108426
76.0%
ValueCountFrequency (%)
1 1820
1.3%
2 3111
2.2%
3 3201
2.2%
4 2755
1.9%
5 3253
2.3%
6 3287
2.3%
7 3209
2.3%
8 3257
2.3%
9 3201
2.2%
10 3365
2.4%
ValueCountFrequency (%)
53 69
 
< 0.1%
52 724
 
0.5%
51 3016
2.1%
50 3133
2.2%
49 2978
2.1%
48 2970
2.1%
47 3011
2.1%
46 3107
2.2%
45 2624
1.8%
44 2324
1.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Not Rebooked
116141 
Rebooked
26447 

Length

Max length12
Median length12
Mean length11.258086
Min length8

Characters and Unicode

Total characters1605268
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Rebooked
2nd rowNot Rebooked
3rd rowNot Rebooked
4th rowNot Rebooked
5th rowNot Rebooked

Common Values

ValueCountFrequency (%)
Not Rebooked 116141
81.5%
Rebooked 26447
 
18.5%

Length

2024-06-09T16:07:51.763373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-09T16:07:51.863254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
rebooked 142588
55.1%
not 116141
44.9%

Most occurring characters

ValueCountFrequency (%)
o 401317
25.0%
e 285176
17.8%
R 142588
 
8.9%
b 142588
 
8.9%
k 142588
 
8.9%
d 142588
 
8.9%
N 116141
 
7.2%
t 116141
 
7.2%
116141
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1605268
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 401317
25.0%
e 285176
17.8%
R 142588
 
8.9%
b 142588
 
8.9%
k 142588
 
8.9%
d 142588
 
8.9%
N 116141
 
7.2%
t 116141
 
7.2%
116141
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1605268
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 401317
25.0%
e 285176
17.8%
R 142588
 
8.9%
b 142588
 
8.9%
k 142588
 
8.9%
d 142588
 
8.9%
N 116141
 
7.2%
t 116141
 
7.2%
116141
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1605268
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 401317
25.0%
e 285176
17.8%
R 142588
 
8.9%
b 142588
 
8.9%
k 142588
 
8.9%
d 142588
 
8.9%
N 116141
 
7.2%
t 116141
 
7.2%
116141
 
7.2%

next_appointment_time
Date

MISSING 

Distinct13826
Distinct (%)52.3%
Missing116141
Missing (%)81.5%
Memory size1.1 MiB
Minimum2020-10-26 10:00:00
Maximum2026-01-01 09:30:00
2024-06-09T16:07:51.965025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:52.113257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

notice
Real number (ℝ)

ZEROS 

Distinct14995
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-841.51892
Minimum-65869.14
Maximum17186.2
Zeros119702
Zeros (%)83.9%
Negative7672
Negative (%)5.4%
Memory size1.1 MiB
2024-06-09T16:07:52.247281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-65869.14
5-th percentile-1042.24
Q10
median0
Q30
95-th percentile27.1265
Maximum17186.2
Range83055.34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4682.9759
Coefficient of variation (CV)-5.5649086
Kurtosis64.831787
Mean-841.51892
Median Absolute Deviation (MAD)0
Skewness-7.2020881
Sum-1.199905 × 108
Variance21930263
MonotonicityNot monotonic
2024-06-09T16:07:52.364982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 119702
83.9%
23.58 16
 
< 0.1%
23.68 15
 
< 0.1%
22.79 14
 
< 0.1%
1.76 14
 
< 0.1%
23.27 14
 
< 0.1%
23.86 13
 
< 0.1%
1.77 13
 
< 0.1%
23.42 13
 
< 0.1%
23.48 13
 
< 0.1%
Other values (14985) 22761
 
16.0%
ValueCountFrequency (%)
-65869.14 1
< 0.1%
-65863.14 1
< 0.1%
-65692.14 1
< 0.1%
-65578.64 1
< 0.1%
-65482.64 1
< 0.1%
-65476.14 1
< 0.1%
-65142.64 1
< 0.1%
-65020.64 1
< 0.1%
-64684.14 1
< 0.1%
-63829.14 1
< 0.1%
ValueCountFrequency (%)
17186.2 1
< 0.1%
16850.2 1
< 0.1%
16514.2 1
< 0.1%
16178.2 1
< 0.1%
15842.2 1
< 0.1%
15506.2 1
< 0.1%
15170.2 1
< 0.1%
14834.2 1
< 0.1%
14498.2 1
< 0.1%
14162.2 1
< 0.1%

cancelled_at
Date

MISSING 

Distinct15540
Distinct (%)67.9%
Missing119696
Missing (%)83.9%
Memory size1.1 MiB
Minimum2020-07-16 08:36:13+00:00
Maximum2024-06-08 02:48:31+00:00
2024-06-09T16:07:52.481571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:52.613075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct580
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2024-06-09T16:07:52.947985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length125
Median length98
Mean length46.628188
Min length4

Characters and Unicode

Total characters6648620
Distinct characters85
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)0.1%

Sample

1st row✅20 Minute Follow Up Consult
2nd row✅20 Minute Follow Up Consult
3rd row✅20 Minute Follow Up Consult
4th row✅20 Minute Follow Up Consult
5th row✅20 Minute Follow Up Consult
ValueCountFrequency (%)
115294
 
11.9%
subsequent 64724
 
6.7%
consultation 63100
 
6.5%
consult 61333
 
6.3%
2 51540
 
5.3%
up 49287
 
5.1%
follow 49211
 
5.1%
minute 44288
 
4.6%
physiotherapy 37332
 
3.8%
30 28483
 
2.9%
Other values (384) 405940
41.8%
2024-06-09T16:07:53.553411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
833188
 
12.5%
t 527489
 
7.9%
o 463384
 
7.0%
n 434612
 
6.5%
e 428083
 
6.4%
i 366282
 
5.5%
s 362839
 
5.5%
u 328294
 
4.9%
l 309964
 
4.7%
a 278059
 
4.2%
Other values (75) 2316426
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6648620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
833188
 
12.5%
t 527489
 
7.9%
o 463384
 
7.0%
n 434612
 
6.5%
e 428083
 
6.4%
i 366282
 
5.5%
s 362839
 
5.5%
u 328294
 
4.9%
l 309964
 
4.7%
a 278059
 
4.2%
Other values (75) 2316426
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6648620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
833188
 
12.5%
t 527489
 
7.9%
o 463384
 
7.0%
n 434612
 
6.5%
e 428083
 
6.4%
i 366282
 
5.5%
s 362839
 
5.5%
u 328294
 
4.9%
l 309964
 
4.7%
a 278059
 
4.2%
Other values (75) 2316426
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6648620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
833188
 
12.5%
t 527489
 
7.9%
o 463384
 
7.0%
n 434612
 
6.5%
e 428083
 
6.4%
i 366282
 
5.5%
s 362839
 
5.5%
u 328294
 
4.9%
l 309964
 
4.7%
a 278059
 
4.2%
Other values (75) 2316426
34.8%

billable_item
Text

MISSING 

Distinct427
Distinct (%)0.3%
Missing3942
Missing (%)2.8%
Memory size1.1 MiB
2024-06-09T16:07:53.893048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length125
Median length102
Mean length44.604648
Min length11

Characters and Unicode

Total characters6184256
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)< 0.1%

Sample

1st rowPhysioCall - 20 Minute Follow Up Consult (Pre-paid)
2nd rowPhysioCall - 20 Minute Follow Up Consult (Pre-paid)
3rd rowPhysioCall - 20 Minute Follow Up Consult (Pre-paid)
4th rowPhysioCall - 20 Minute Follow Up Consult (Pre-paid)
5th rowPhysioCall - 20 Minute Follow Up Consult (Pre-paid)
ValueCountFrequency (%)
117300
 
13.7%
subsequent 64758
 
7.6%
consultation 62269
 
7.3%
consult 59582
 
7.0%
up 47246
 
5.5%
follow 47046
 
5.5%
osteopathy 43367
 
5.1%
minute 42426
 
5.0%
physiotherapy 35882
 
4.2%
30 29428
 
3.4%
Other values (286) 307635
35.9%
2024-06-09T16:07:54.409073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
739173
 
12.0%
t 516423
 
8.4%
o 453326
 
7.3%
n 426036
 
6.9%
e 403064
 
6.5%
i 359012
 
5.8%
s 358798
 
5.8%
u 322138
 
5.2%
l 308684
 
5.0%
a 274125
 
4.4%
Other values (63) 2023477
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6184256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
739173
 
12.0%
t 516423
 
8.4%
o 453326
 
7.3%
n 426036
 
6.9%
e 403064
 
6.5%
i 359012
 
5.8%
s 358798
 
5.8%
u 322138
 
5.2%
l 308684
 
5.0%
a 274125
 
4.4%
Other values (63) 2023477
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6184256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
739173
 
12.0%
t 516423
 
8.4%
o 453326
 
7.3%
n 426036
 
6.9%
e 403064
 
6.5%
i 359012
 
5.8%
s 358798
 
5.8%
u 322138
 
5.2%
l 308684
 
5.0%
a 274125
 
4.4%
Other values (63) 2023477
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6184256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
739173
 
12.0%
t 516423
 
8.4%
o 453326
 
7.3%
n 426036
 
6.9%
e 403064
 
6.5%
i 359012
 
5.8%
s 358798
 
5.8%
u 322138
 
5.2%
l 308684
 
5.0%
a 274125
 
4.4%
Other values (63) 2023477
32.7%

category
Text

MISSING 

Distinct161
Distinct (%)0.1%
Missing24476
Missing (%)17.2%
Memory size1.1 MiB
2024-06-09T16:07:54.791539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length68
Median length62
Mean length34.101751
Min length3

Characters and Unicode

Total characters4027826
Distinct characters91
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st rowA | Pre Pay Online & Save ✅
2nd rowA | Pre Pay Online & Save ✅
3rd rowA | Pre Pay Online & Save ✅
4th rowA | Pre Pay Online & Save ✅
5th rowA | Pre Pay Online & Save ✅
ValueCountFrequency (%)
71336
 
11.8%
2 42860
 
7.1%
subsequent 37632
 
6.2%
consultations 37277
 
6.2%
for 37115
 
6.1%
osteopathy 36551
 
6.0%
existing 35938
 
5.9%
patients 35938
 
5.9%
physiotherapy 27515
 
4.5%
a 26225
 
4.3%
Other values (205) 216406
35.8%
2024-06-09T16:07:55.279666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
488300
 
12.1%
t 330438
 
8.2%
s 285905
 
7.1%
e 231211
 
5.7%
n 213718
 
5.3%
o 192977
 
4.8%
i 191787
 
4.8%
a 177585
 
4.4%
P 130288
 
3.2%
u 126705
 
3.1%
Other values (81) 1658912
41.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4027826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
488300
 
12.1%
t 330438
 
8.2%
s 285905
 
7.1%
e 231211
 
5.7%
n 213718
 
5.3%
o 192977
 
4.8%
i 191787
 
4.8%
a 177585
 
4.4%
P 130288
 
3.2%
u 126705
 
3.1%
Other values (81) 1658912
41.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4027826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
488300
 
12.1%
t 330438
 
8.2%
s 285905
 
7.1%
e 231211
 
5.7%
n 213718
 
5.3%
o 192977
 
4.8%
i 191787
 
4.8%
a 177585
 
4.4%
P 130288
 
3.2%
u 126705
 
3.1%
Other values (81) 1658912
41.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4027826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
488300
 
12.1%
t 330438
 
8.2%
s 285905
 
7.1%
e 231211
 
5.7%
n 213718
 
5.3%
o 192977
 
4.8%
i 191787
 
4.8%
a 177585
 
4.4%
P 130288
 
3.2%
u 126705
 
3.1%
Other values (81) 1658912
41.2%

cancelled
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size139.4 KiB
False
119696 
True
22892 
ValueCountFrequency (%)
False 119696
83.9%
True 22892
 
16.1%
2024-06-09T16:07:55.391370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

missed
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size139.4 KiB
False
140606 
True
 
1982
ValueCountFrequency (%)
False 140606
98.6%
True 1982
 
1.4%
2024-06-09T16:07:55.466469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Distinct62819
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2009-08-15 01:30:00+00:00
Maximum2025-12-31 23:30:00+00:00
2024-06-09T16:07:55.580772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:55.726009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

business_name
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Scarb Physio & Health
32219 
All Sorted Physiotherapy
29681 
Osteopathy | Melton
26974 
Osteopathy | Bacchus Marsh
17735 
Scarb Remedial Massage
8732 
Other values (20)
27247 

Length

Max length30
Median length26
Mean length21.578597
Min length12

Characters and Unicode

Total characters3076849
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPhysioCall Gladstone
2nd rowPhysioCall Gladstone
3rd rowPhysioCall Gladstone
4th rowPhysioCall Gladstone
5th rowPhysioCall Gladstone

Common Values

ValueCountFrequency (%)
Scarb Physio & Health 32219
22.6%
All Sorted Physiotherapy 29681
20.8%
Osteopathy | Melton 26974
18.9%
Osteopathy | Bacchus Marsh 17735
12.4%
Scarb Remedial Massage 8732
 
6.1%
PhysioCall Gladstone 6252
 
4.4%
Bosch Psychology 5753
 
4.0%
Newport Physiotherapy 5153
 
3.6%
Newport Psychology 4207
 
3.0%
Physiotherapy | Melton 1407
 
1.0%
Other values (15) 4475
 
3.1%

Length

2024-06-09T16:07:55.847501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
80527
17.6%
osteopathy 44709
9.8%
scarb 41897
9.2%
physiotherapy 36410
8.0%
health 32873
7.2%
physio 32219
 
7.0%
all 29681
 
6.5%
sorted 29681
 
6.5%
melton 29331
 
6.4%
bacchus 18375
 
4.0%
Other values (25) 81710
17.9%

Most occurring characters

ValueCountFrequency (%)
314825
 
10.2%
h 242304
 
7.9%
a 235913
 
7.7%
t 235743
 
7.7%
o 225274
 
7.3%
e 218725
 
7.1%
s 202344
 
6.6%
y 177814
 
5.8%
l 161340
 
5.2%
r 137624
 
4.5%
Other values (37) 924943
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3076849
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
314825
 
10.2%
h 242304
 
7.9%
a 235913
 
7.7%
t 235743
 
7.7%
o 225274
 
7.3%
e 218725
 
7.1%
s 202344
 
6.6%
y 177814
 
5.8%
l 161340
 
5.2%
r 137624
 
4.5%
Other values (37) 924943
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3076849
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
314825
 
10.2%
h 242304
 
7.9%
a 235913
 
7.7%
t 235743
 
7.7%
o 225274
 
7.3%
e 218725
 
7.1%
s 202344
 
6.6%
y 177814
 
5.8%
l 161340
 
5.2%
r 137624
 
4.5%
Other values (37) 924943
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3076849
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
314825
 
10.2%
h 242304
 
7.9%
a 235913
 
7.7%
t 235743
 
7.7%
o 225274
 
7.3%
e 218725
 
7.1%
s 202344
 
6.6%
y 177814
 
5.8%
l 161340
 
5.2%
r 137624
 
4.5%
Other values (37) 924943
30.1%

customer_type
Categorical

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)0.1%
Missing103758
Missing (%)72.8%
Memory size1.1 MiB
WorkCover
5945 
NDIS
5638 
Concession / Pension Card Holder
5346 
Medicare Referrals
4281 
DVA
3120 
Other values (30)
14500 

Length

Max length32
Median length23
Mean length12.327221
Min length3

Characters and Unicode

Total characters478666
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStaff
2nd rowStaff
3rd rowHigh Risk
4th rowHigh Risk
5th rowStaff

Common Values

ValueCountFrequency (%)
WorkCover 5945
 
4.2%
NDIS 5638
 
4.0%
Concession / Pension Card Holder 5346
 
3.7%
Medicare Referrals 4281
 
3.0%
DVA 3120
 
2.2%
NDIS Customer 2474
 
1.7%
CDM 2449
 
1.7%
DVA Card Holder 2188
 
1.5%
Third Party 1716
 
1.2%
Student 1531
 
1.1%
Other values (25) 4142
 
2.9%
(Missing) 103758
72.8%

Length

2024-06-09T16:07:55.981467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ndis 8112
10.6%
card 7538
9.8%
holder 7534
9.8%
workcover 6158
8.0%
5923
 
7.7%
medicare 5621
 
7.3%
concession 5408
 
7.1%
pension 5346
 
7.0%
dva 5308
 
6.9%
referrals 4281
 
5.6%
Other values (37) 15375
20.1%

Most occurring characters

ValueCountFrequency (%)
e 50709
 
10.6%
r 49464
 
10.3%
o 39555
 
8.3%
38209
 
8.0%
d 24831
 
5.2%
C 24721
 
5.2%
n 24050
 
5.0%
s 24023
 
5.0%
a 21273
 
4.4%
i 19786
 
4.1%
Other values (36) 162045
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 478666
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 50709
 
10.6%
r 49464
 
10.3%
o 39555
 
8.3%
38209
 
8.0%
d 24831
 
5.2%
C 24721
 
5.2%
n 24050
 
5.0%
s 24023
 
5.0%
a 21273
 
4.4%
i 19786
 
4.1%
Other values (36) 162045
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 478666
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 50709
 
10.6%
r 49464
 
10.3%
o 39555
 
8.3%
38209
 
8.0%
d 24831
 
5.2%
C 24721
 
5.2%
n 24050
 
5.0%
s 24023
 
5.0%
a 21273
 
4.4%
i 19786
 
4.1%
Other values (36) 162045
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 478666
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 50709
 
10.6%
r 49464
 
10.3%
o 39555
 
8.3%
38209
 
8.0%
d 24831
 
5.2%
C 24721
 
5.2%
n 24050
 
5.0%
s 24023
 
5.0%
a 21273
 
4.4%
i 19786
 
4.1%
Other values (36) 162045
33.9%

title
Categorical

IMBALANCE  MISSING 

Distinct33
Distinct (%)< 0.1%
Missing58854
Missing (%)41.3%
Memory size1.1 MiB
Mr
31422 
Mrs
21231 
Ms
17166 
Miss
8307 
Master
 
1545
Other values (28)
4063 

Length

Max length15
Median length2
Mean length2.5610027
Min length1

Characters and Unicode

Total characters214443
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowMr
2nd rowMr
3rd rowMiss
4th rowMr
5th rowMiss

Common Values

ValueCountFrequency (%)
Mr 31422
22.0%
Mrs 21231
 
14.9%
Ms 17166
 
12.0%
Miss 8307
 
5.8%
Master 1545
 
1.1%
1470
 
1.0%
MISS 928
 
0.7%
Dr 681
 
0.5%
MASTER 355
 
0.2%
Mrs 185
 
0.1%
Other values (23) 444
 
0.3%
(Missing) 58854
41.3%

Length

2024-06-09T16:07:56.094365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mr 31635
38.5%
mrs 21417
26.0%
ms 17236
21.0%
miss 9241
 
11.2%
master 1900
 
2.3%
dr 681
 
0.8%
mast 70
 
0.1%
maste 22
 
< 0.1%
mx 17
 
< 0.1%
sr 10
 
< 0.1%
Other values (9) 32
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
M 81536
38.0%
s 56920
26.5%
r 55286
25.8%
i 8317
 
3.9%
S 2225
 
1.0%
2153
 
1.0%
a 1651
 
0.8%
t 1645
 
0.8%
e 1580
 
0.7%
I 928
 
0.4%
Other values (16) 2202
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 214443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 81536
38.0%
s 56920
26.5%
r 55286
25.8%
i 8317
 
3.9%
S 2225
 
1.0%
2153
 
1.0%
a 1651
 
0.8%
t 1645
 
0.8%
e 1580
 
0.7%
I 928
 
0.4%
Other values (16) 2202
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 214443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 81536
38.0%
s 56920
26.5%
r 55286
25.8%
i 8317
 
3.9%
S 2225
 
1.0%
2153
 
1.0%
a 1651
 
0.8%
t 1645
 
0.8%
e 1580
 
0.7%
I 928
 
0.4%
Other values (16) 2202
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 214443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 81536
38.0%
s 56920
26.5%
r 55286
25.8%
i 8317
 
3.9%
S 2225
 
1.0%
2153
 
1.0%
a 1651
 
0.8%
t 1645
 
0.8%
e 1580
 
0.7%
I 928
 
0.4%
Other values (16) 2202
 
1.0%

state
Text

MISSING 

Distinct74
Distinct (%)0.1%
Missing71910
Missing (%)50.4%
Memory size1.1 MiB
2024-06-09T16:07:56.264241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length17
Median length3
Mean length3.1782026
Min length1

Characters and Unicode

Total characters224629
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowQLD
2nd rowQLD
3rd rowQLD
4th rowQLD
5th rowQLD
ValueCountFrequency (%)
qld 61584
86.9%
vic 6462
 
9.1%
queensland 1293
 
1.8%
nsw 785
 
1.1%
west 155
 
0.2%
gladstone 153
 
0.2%
victoria 107
 
0.2%
wa 47
 
0.1%
newport 22
 
< 0.1%
sa 19
 
< 0.1%
Other values (55) 247
 
0.3%
2024-06-09T16:07:56.599796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Q 62791
28.0%
L 58100
25.9%
D 58081
25.9%
V 6567
 
2.9%
I 5603
 
2.5%
C 5448
 
2.4%
d 5014
 
2.2%
l 5008
 
2.2%
e 2993
 
1.3%
n 2820
 
1.3%
Other values (46) 12204
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 224629
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Q 62791
28.0%
L 58100
25.9%
D 58081
25.9%
V 6567
 
2.9%
I 5603
 
2.5%
C 5448
 
2.4%
d 5014
 
2.2%
l 5008
 
2.2%
e 2993
 
1.3%
n 2820
 
1.3%
Other values (46) 12204
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 224629
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Q 62791
28.0%
L 58100
25.9%
D 58081
25.9%
V 6567
 
2.9%
I 5603
 
2.5%
C 5448
 
2.4%
d 5014
 
2.2%
l 5008
 
2.2%
e 2993
 
1.3%
n 2820
 
1.3%
Other values (46) 12204
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 224629
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Q 62791
28.0%
L 58100
25.9%
D 58081
25.9%
V 6567
 
2.9%
I 5603
 
2.5%
C 5448
 
2.4%
d 5014
 
2.2%
l 5008
 
2.2%
e 2993
 
1.3%
n 2820
 
1.3%
Other values (46) 12204
 
5.4%

date_of_birth
Date

MISSING 

Distinct18931
Distinct (%)13.5%
Missing2578
Missing (%)1.8%
Memory size1.1 MiB
Minimum1904-01-19 00:00:00
Maximum2024-04-22 00:00:00
2024-06-09T16:07:56.728748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:56.867823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

sex
Categorical

IMBALANCE  MISSING 

Distinct8
Distinct (%)< 0.1%
Missing49112
Missing (%)34.4%
Memory size1.1 MiB
Female
56870 
Male
34880 
9
 
1405
female
 
152
male
 
143
Other values (3)
 
26

Length

Max length6
Median length6
Mean length5.1750931
Min length1

Characters and Unicode

Total characters483747
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 56870
39.9%
Male 34880
24.5%
9 1405
 
1.0%
female 152
 
0.1%
male 143
 
0.1%
Other 13
 
< 0.1%
AMAB 12
 
< 0.1%
other 1
 
< 0.1%
(Missing) 49112
34.4%

Length

2024-06-09T16:07:56.984884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-09T16:07:57.077099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
female 57022
61.0%
male 35023
37.5%
9 1405
 
1.5%
other 14
 
< 0.1%
amab 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 149081
30.8%
a 92045
19.0%
l 92045
19.0%
m 57165
 
11.8%
F 56870
 
11.8%
M 34892
 
7.2%
9 1405
 
0.3%
f 152
 
< 0.1%
A 24
 
< 0.1%
t 14
 
< 0.1%
Other values (5) 54
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 483747
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 149081
30.8%
a 92045
19.0%
l 92045
19.0%
m 57165
 
11.8%
F 56870
 
11.8%
M 34892
 
7.2%
9 1405
 
0.3%
f 152
 
< 0.1%
A 24
 
< 0.1%
t 14
 
< 0.1%
Other values (5) 54
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 483747
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 149081
30.8%
a 92045
19.0%
l 92045
19.0%
m 57165
 
11.8%
F 56870
 
11.8%
M 34892
 
7.2%
9 1405
 
0.3%
f 152
 
< 0.1%
A 24
 
< 0.1%
t 14
 
< 0.1%
Other values (5) 54
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 483747
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 149081
30.8%
a 92045
19.0%
l 92045
19.0%
m 57165
 
11.8%
F 56870
 
11.8%
M 34892
 
7.2%
9 1405
 
0.3%
f 152
 
< 0.1%
A 24
 
< 0.1%
t 14
 
< 0.1%
Other values (5) 54
 
< 0.1%

post_code
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing51112
Missing (%)35.8%
Memory size1.1 MiB

city
Text

MISSING 

Distinct901
Distinct (%)1.0%
Missing49994
Missing (%)35.1%
Memory size1.1 MiB
2024-06-09T16:07:57.389089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length43
Median length25
Mean length9.4585826
Min length2

Characters and Unicode

Total characters875808
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)0.2%

Sample

1st rowSouth Gladstone
2nd rowClinton
3rd rowGladstone
4th rowSouth Gladstone
5th rowToolooa
ValueCountFrequency (%)
scarborough 17321
 
15.4%
gladstone 16019
 
14.3%
newport 8419
 
7.5%
redcliffe 7433
 
6.6%
margate 3798
 
3.4%
clontarf 2991
 
2.7%
bay 2570
 
2.3%
deception 2523
 
2.2%
kippa 2198
 
2.0%
ring 2198
 
2.0%
Other values (627) 46856
41.7%
2024-06-09T16:07:57.915583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 64155
 
7.3%
r 56642
 
6.5%
a 48844
 
5.6%
e 48273
 
5.5%
S 37112
 
4.2%
N 36826
 
4.2%
c 28424
 
3.2%
t 27851
 
3.2%
l 27652
 
3.2%
A 27031
 
3.1%
Other values (59) 472998
54.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 875808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 64155
 
7.3%
r 56642
 
6.5%
a 48844
 
5.6%
e 48273
 
5.5%
S 37112
 
4.2%
N 36826
 
4.2%
c 28424
 
3.2%
t 27851
 
3.2%
l 27652
 
3.2%
A 27031
 
3.1%
Other values (59) 472998
54.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 875808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 64155
 
7.3%
r 56642
 
6.5%
a 48844
 
5.6%
e 48273
 
5.5%
S 37112
 
4.2%
N 36826
 
4.2%
c 28424
 
3.2%
t 27851
 
3.2%
l 27652
 
3.2%
A 27031
 
3.1%
Other values (59) 472998
54.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 875808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 64155
 
7.3%
r 56642
 
6.5%
a 48844
 
5.6%
e 48273
 
5.5%
S 37112
 
4.2%
N 36826
 
4.2%
c 28424
 
3.2%
t 27851
 
3.2%
l 27652
 
3.2%
A 27031
 
3.1%
Other values (59) 472998
54.0%

occupation
Text

MISSING 

Distinct3868
Distinct (%)5.2%
Missing68082
Missing (%)47.7%
Memory size1.1 MiB
2024-06-09T16:07:58.209814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length57
Median length45
Mean length11.767549
Min length1

Characters and Unicode

Total characters876753
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1322 ?
Unique (%)1.8%

Sample

1st rowPhysio
2nd rowRemedial massage therapist
3rd rowQAL Operator
4th rowPhysio
5th rowStudent
ValueCountFrequency (%)
retired 14268
 
12.2%
student 5763
 
4.9%
manager 4207
 
3.6%
teacher 3681
 
3.1%
worker 2161
 
1.8%
nurse 2001
 
1.7%
officer 1833
 
1.6%
assistant 1816
 
1.6%
driver 1562
 
1.3%
support 1432
 
1.2%
Other values (2083) 78373
66.9%
2024-06-09T16:07:58.666921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 117204
13.4%
r 85633
 
9.8%
t 74321
 
8.5%
i 66308
 
7.6%
a 55452
 
6.3%
n 50426
 
5.8%
44914
 
5.1%
o 38126
 
4.3%
d 36019
 
4.1%
s 35166
 
4.0%
Other values (70) 273184
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 876753
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 117204
13.4%
r 85633
 
9.8%
t 74321
 
8.5%
i 66308
 
7.6%
a 55452
 
6.3%
n 50426
 
5.8%
44914
 
5.1%
o 38126
 
4.3%
d 36019
 
4.1%
s 35166
 
4.0%
Other values (70) 273184
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 876753
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 117204
13.4%
r 85633
 
9.8%
t 74321
 
8.5%
i 66308
 
7.6%
a 55452
 
6.3%
n 50426
 
5.8%
44914
 
5.1%
o 38126
 
4.3%
d 36019
 
4.1%
s 35166
 
4.0%
Other values (70) 273184
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 876753
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 117204
13.4%
r 85633
 
9.8%
t 74321
 
8.5%
i 66308
 
7.6%
a 55452
 
6.3%
n 50426
 
5.8%
44914
 
5.1%
o 38126
 
4.3%
d 36019
 
4.1%
s 35166
 
4.0%
Other values (70) 273184
31.2%

referred
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size139.4 KiB
False
97591 
True
44997 
ValueCountFrequency (%)
False 97591
68.4%
True 44997
31.6%
2024-06-09T16:07:58.783926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Interactions

2024-06-09T16:07:45.713122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:41.082896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:41.830105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:42.583072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:43.398040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:44.162118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:44.896055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:45.813093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:41.199700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:41.946880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:42.718336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:43.509215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:44.264065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:45.000288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:45.915903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:41.315110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:42.049365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:42.842089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:43.632513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:44.366991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:45.098629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:46.017614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:41.435641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:42.148936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:42.965483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:43.747347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:44.465659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:45.299877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:46.131799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:41.550738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:42.265776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:43.078926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:43.857268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:44.583579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:45.414921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:46.233040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:41.650143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:42.379217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:43.197845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:43.962559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:44.682198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:45.513634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:46.314250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:41.734256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:42.482444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:43.296971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:44.062839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:44.782374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-09T16:07:45.599289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-06-09T16:07:58.864591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
appointment_statusbusiness_namecancelledcase_linkedcase_typecustomer_typeday_of_monthday_of_weekmissedmonth_of_yearmonth_periodnoticepatient_statuspatient_typereferredsextime_of_daytitletotal_cxl_appointments_before_appointmenttotal_good_appointments_before_appointmenttotal_open_invoices_before_appointemntweek_of_year
1.0000.1051.0000.1830.1470.1910.7870.0020.1280.0210.1070.011-0.1270.0710.1490.4000.0590.1260.222-0.255-0.197-0.095-0.009
appointment_status0.1051.0000.3250.0160.2860.0220.4450.0000.0610.0160.0400.0000.0960.1960.1380.1210.0850.0830.1010.2790.3680.261-0.017
business_name1.0000.3251.0000.2230.6730.3060.325-0.0020.1130.0420.0660.0200.2590.1180.1600.6710.1070.1800.121-0.0480.0640.080-0.036
cancelled0.1830.0160.2231.0000.1040.0610.114-0.0030.0240.0520.0180.0010.3280.0190.1820.0000.0520.0540.0790.2780.0410.0260.004
case_linked0.1470.2860.6730.1041.0001.0000.352-0.0010.0930.0320.0960.0000.1800.2010.0830.4430.1850.1450.2450.1230.3310.128-0.078
case_type0.1910.0220.3060.0611.0001.0000.342-0.0050.0680.0310.0330.0040.0640.0370.0400.1280.0310.0690.0500.0280.0580.005-0.017
customer_type0.7870.4450.3250.1140.3520.3421.0000.0020.1170.0630.0550.012-0.0280.1620.1320.4360.1820.1900.1890.1100.1910.280-0.023
day_of_month0.0020.000-0.002-0.003-0.001-0.0050.0021.0000.0350.0040.0730.9640.0030.0030.0070.0060.0010.0050.000-0.005-0.0050.0080.050
day_of_week0.1280.0610.1130.0240.0930.0680.1170.0351.0000.0060.0270.0310.0120.0150.0630.0740.0230.1370.0340.0200.0380.006-0.017
missed0.0210.0160.0420.0520.0320.0310.0630.0040.0061.0000.0230.000-0.0170.0260.0440.0200.0380.0070.0350.0140.0200.029-0.002
month_of_year0.1070.0400.0660.0180.0960.0330.0550.0730.0270.0231.0000.078-0.0270.0560.0450.0510.0130.0100.0210.012-0.017-0.0090.308
month_period0.0110.0000.0200.0010.0000.0040.0120.9640.0310.0000.0781.000-0.0020.0040.0060.0010.0000.0000.000-0.001-0.002-0.0010.035
notice-0.1270.0960.2590.3280.1800.064-0.0280.0030.012-0.017-0.027-0.0021.0000.0230.0790.1330.0210.0420.072-0.1390.0230.048-0.033
patient_status0.0710.1960.1180.0190.2010.0370.1620.0030.0150.0260.0560.0040.0231.0000.0450.1280.0310.0280.0440.0800.096-0.012-0.065
patient_type0.1490.1380.1600.1820.0830.0400.1320.0070.0630.0440.0450.0060.0790.0451.0000.0840.0270.1220.048-0.055-0.035-0.0270.023
referred0.4000.1210.6710.0000.4430.1280.4360.0060.0740.0200.0510.0010.1330.1280.0841.0000.1230.1540.2150.0710.1060.031-0.021
sex0.0590.0850.1070.0520.1850.0310.1820.0010.0230.0380.0130.0000.0210.0310.0270.1231.0000.0490.442-0.0690.0030.067-0.011
time_of_day0.1260.0830.1800.0540.1450.0690.1900.0050.1370.0070.0100.0000.0420.0280.1220.1540.0491.0000.0890.0420.0210.0290.002
title0.2220.1010.1210.0790.2450.0500.1890.0000.0340.0350.0210.0000.0720.0440.0480.2150.4420.0891.0000.0830.0710.020-0.012
total_cxl_appointments_before_appointment-0.2550.279-0.0480.2780.1230.0280.110-0.0050.0200.0140.012-0.001-0.1390.080-0.0550.071-0.0690.0420.0831.0000.5700.1920.020
total_good_appointments_before_appointment-0.1970.3680.0640.0410.3310.0580.191-0.0050.0380.020-0.017-0.0020.0230.096-0.0350.1060.0030.0210.0710.5701.0000.252-0.015
total_open_invoices_before_appointemnt-0.0950.2610.0800.0260.1280.0050.2800.0080.0060.029-0.009-0.0010.048-0.012-0.0270.0310.0670.0290.0200.1920.2521.000-0.017
week_of_year-0.009-0.017-0.0360.004-0.078-0.017-0.0230.050-0.017-0.0020.3080.035-0.033-0.0650.023-0.021-0.0110.002-0.0120.020-0.015-0.0171.000

Missing values

2024-06-09T16:07:46.553620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-09T16:07:47.180494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-09T16:07:48.178023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

case_linkedcase_typetotal_open_invoices_before_appointemnttotal_good_appointments_before_appointmenttotal_cxl_appointments_before_appointmentpatient_statuspatient_typetime_of_daymonth_periodday_of_weekmonth_of_yearday_of_monthweek_of_yearappointment_statusnext_appointment_timenoticecancelled_atappointment_typebillable_itemcategorycancelledmissedappointment_start_timebusiness_namecustomer_typetitlestatedate_of_birthsexpost_codecityoccupationreferred
011NoNaN000Not Yet ActionedNew To ClinicEarly MorningMid monthFridayJuly17.029.0Not RebookedNaN12.732020-07-16 08:36:13+00✅20 Minute Follow Up ConsultPhysioCall - 20 Minute Follow Up Consult (Pre-paid)A | Pre Pay Online & Save ✅YesNo2020-07-16 21:20:00+00PhysioCall GladstoneStaffMrQLD1984-02-03Male4680.0South GladstonePhysioYes
111NoNaN000Not Yet ActionedRecurringLate AfternoonMid monthThursdayAugust13.033.0Not RebookedNaN0.00NaN✅20 Minute Follow Up ConsultPhysioCall - 20 Minute Follow Up Consult (Pre-paid)A | Pre Pay Online & Save ✅NoNo2020-08-13 06:00:00+00PhysioCall GladstoneNaNNaNNaN1997-01-30NaNNaNNaNNaNNo
211NoNaN010Not Yet ActionedRecurringLate MorningMid monthThursdayAugust20.034.0Not RebookedNaN0.00NaN✅20 Minute Follow Up ConsultPhysioCall - 20 Minute Follow Up Consult (Pre-paid)A | Pre Pay Online & Save ✅NoNo2020-08-20 01:20:00+00PhysioCall GladstoneNaNNaNNaN1997-01-30NaNNaNNaNNaNNo
311NoNaN020Not Yet ActionedRecurringLate MorningMid monthFridaySeptember18.038.0Not RebookedNaN0.00NaN✅20 Minute Follow Up ConsultPhysioCall - 20 Minute Follow Up Consult (Pre-paid)A | Pre Pay Online & Save ✅NoNo2020-09-17 23:50:00+00PhysioCall GladstoneNaNNaNNaN1997-01-30NaNNaNNaNNaNNo
411NoNaN000Not Yet ActionedNew To ClinicEarly MorningEnd monthMondaySeptember21.039.0Not RebookedNaN0.00NaN✅20 Minute Follow Up ConsultPhysioCall - 20 Minute Follow Up Consult (Pre-paid)A | Pre Pay Online & Save ✅NoNo2020-09-20 22:20:00+00PhysioCall GladstoneNaNNaNNaN1980-02-03NaNNaNNaNNaNNo
511NoNaN000Not Yet ActionedRecurringLate AfternoonMid monthTuesdayOctober20.043.0Not RebookedNaN0.00NaN✅40 Minute Follow Up ConsultNaN1. PhysioCall - Pre Pay Online & Save ✅NoNo2020-10-20 05:10:00+00PhysioCall GladstoneNaNMrQLD1977-06-30NaN4680.0ClintonRemedial massage therapistYes
611NoNaN000Not Yet Actionednew injury/issueEveningMid monthSundayOctober18.042.0Not RebookedNaN0.00NaN✅60 Minute - Initial Consult MassageNaN1. Gladstone Massage - Prepay Online & Save ✅NoNo2020-10-18 10:30:00+00Gladstone MassageNaNNaNNaN1988-07-09NaNNaNNaNNaNNo
711YesUnlimited000Not Yet ActionedRecurringLate MorningMid monthMondayOctober19.043.0Not RebookedNaN0.00NaN2. 20 Minute Follow Up ConsultPhysioCall - 20 Minute Follow Up ConsultA | Physiotherapy Treatment ❤️‍🩹 (in clinic)NoNo2020-10-19 00:40:00+00PhysioCall GladstoneNaNNaNNaN1979-09-18NaNNaNNaNNaNNo
811NoNaN000Not Yet ActionedRecurringLate MorningMid monthMondayOctober19.043.0Not RebookedNaN0.00NaN✅20 Minute Follow Up ConsultPhysioCall - 20 Minute Follow Up Consult (Pre-paid)A | Pre Pay Online & Save ✅NoNo2020-10-19 01:00:00+00PhysioCall GladstoneNaNNaNNaN1994-01-16NaNNaNNaNNaNNo
911YesUnlimited000Not Yet ActionedRecurringLate MorningMid monthMondayOctober19.043.0Not RebookedNaN0.00NaN40 Minute Follow Up ConsultNaN2. PhysioCall - Pay In ClinicNoNo2020-10-19 01:20:00+00PhysioCall GladstoneNaNMissQLD1994-03-05NaN4680.0GladstoneQAL OperatorYes
case_linkedcase_typetotal_open_invoices_before_appointemnttotal_good_appointments_before_appointmenttotal_cxl_appointments_before_appointmentpatient_statuspatient_typetime_of_daymonth_periodday_of_weekmonth_of_yearday_of_monthweek_of_yearappointment_statusnext_appointment_timenoticecancelled_atappointment_typebillable_itemcategorycancelledmissedappointment_start_timebusiness_namecustomer_typetitlestatedate_of_birthsexpost_codecityoccupationreferred
14257816YesMax Sessions0113Therapist Follow UpRecurringLate AfternoonMid monthSaturdayJuly20.029.0Not RebookedNaN0.0NaN✨ Osteopathy CDM - Subsequent ConsultationOsteopathy CDM - Subsequent ConsultationCDM (Medicare / GP Referrals)NoNo2024-07-20 02:00:00+00Osteopathy | MeltonNaNNaNNaN1994-04-07NaNNaNNaNAdministrationNo
14257916NoNaN021Patient Stopped TreatmentRecurringLate AfternoonEnd monthSaturdayJune22.025.0Not RebookedNaN0.0NaN✨ Osteopathy - Subsequent ConsultationOsteopathy - Subsequent Consultation2. Subsequent Osteopathy Consultations for Existing PatientsNoNo2024-06-22 02:00:00+00Osteopathy | MeltonNaNNaNNaN1979-10-26NaNNaNNaNNaNNo
14258016YesUnlimited0161Has An Upcoming BookingRecurringLate MorningMid monthWednesdayJune19.025.0Rebooked2024-06-22 10:30:000.0NaN2. Physiotherapy WorkSafe - Subsequent ConsultationPhysiotherapy WorkSafe - Subsequent ConsultationWorkSafeNoNo2024-06-19 00:30:00+00Physiotherapy | MeltonNaNMrNaN1989-10-06MaleNaNNaNNaNNo
14258116YesUnlimited0171Has An Upcoming BookingRecurringLate MorningEnd monthSaturdayJune22.025.0Rebooked2024-06-26 10:30:000.0NaN2. Physiotherapy WorkSafe - Subsequent ConsultationPhysiotherapy WorkSafe - Subsequent ConsultationWorkSafeNoNo2024-06-22 00:30:00+00Physiotherapy | MeltonNaNMrNaN1989-10-06MaleNaNNaNNaNNo
14258216YesUnlimited0181Has An Upcoming BookingRecurringLate MorningEnd monthWednesdayJune26.026.0Rebooked2024-06-29 11:00:000.0NaN2. Physiotherapy WorkSafe - Subsequent ConsultationPhysiotherapy WorkSafe - Subsequent ConsultationWorkSafeNoNo2024-06-26 00:30:00+00Physiotherapy | MeltonNaNMrNaN1989-10-06MaleNaNNaNNaNNo
14258316NoNaN0284Admin Follow UpRecurringLate AfternoonMid monthTuesdayJune18.025.0Not RebookedNaN0.0NaN⭐️ Osteopathy - Subsequent Consultation With Senior OsteopathOsteopathy - Subsequent Consultation With Senior Osteopath2. Subsequent Osteopathy Consultations for Existing PatientsNoNo2024-06-18 02:00:00+00Osteopathy | Bacchus MarshNaNNaNNaN1989-02-17FemaleNaNNaNCourierNo
14258416YesUnlimited0191Has An Upcoming BookingRecurringLate MorningEnd monthSaturdayJune29.026.0Not RebookedNaN0.0NaN2. Physiotherapy WorkSafe - Subsequent ConsultationPhysiotherapy WorkSafe - Subsequent ConsultationWorkSafeNoNo2024-06-29 01:00:00+00Physiotherapy | MeltonNaNMrNaN1989-10-06MaleNaNNaNNaNNo
14258516YesUnlimited0100Not Yet ActionedRecurringLate AfternoonMid monthMondayJune17.025.0Not RebookedNaN0.0NaN✨ Osteopathy - Subsequent ConsultationOsteopathy - Subsequent Consultation2. Subsequent Osteopathy Consultations for Existing PatientsNoNo2024-06-17 05:15:00+00Osteopathy | Bacchus MarshNaNMrsVIC1963-12-18Female3340Bacchus MarshWoolworths team memberYes
14258616NoNaN020Not Yet ActionedRecurringEveningMid monthWednesdayJune12.024.0Not RebookedNaN0.0NaN⭐️ Osteopathy - Subsequent Consultation With Senior OsteopathOsteopathy - Subsequent Consultation With Senior Osteopath2. Subsequent Osteopathy Consultations for Existing PatientsNoNo2024-06-12 09:30:00+00Osteopathy | Bacchus MarshNaNNaNNaN1968-05-28NaNNaNNaNNaNNo
14258716YesUnlimited0174Therapist Follow UpRecurringLate AfternoonMid monthWednesdayJune19.025.0Not RebookedNaN0.0NaN⭐️ Osteopathy - Subsequent Consultation With Senior OsteopathOsteopathy - Subsequent Consultation With Senior Osteopath2. Subsequent Osteopathy Consultations for Existing PatientsNoNo2024-06-19 05:00:00+00Osteopathy | MeltonNDISNaNVIc1955-11-08Female3338Melton SouthSelf EmployedNo

Duplicate rows

Most frequently occurring

case_linkedcase_typetotal_open_invoices_before_appointemnttotal_good_appointments_before_appointmenttotal_cxl_appointments_before_appointmentpatient_statuspatient_typetime_of_daymonth_periodday_of_weekmonth_of_yearday_of_monthweek_of_yearappointment_statusnext_appointment_timenoticecancelled_atappointment_typebillable_itemcategorycancelledmissedappointment_start_timebusiness_namecustomer_typetitlestatedate_of_birthsexcityoccupationreferred# duplicates
013NoNaN002Not Yet ActionedNew To ClinicLate AfternoonEnd monthMondayMay21.021.0Not RebookedNaN-25905.132021-05-04 13:07:52+00Initial Consultation week dayInitial Consultation week dayNaNYesNo2018-05-21 04:00:00+00All Sorted PhysiotherapyNaNNaNNaNNaNMaleCALLIOPENaNNo2
113NoNaN005Not Yet ActionedNew To ClinicLate AfternoonMid monthThursdayJune14.024.0Not RebookedNaN-25331.132021-05-04 13:07:47+00Initial Consultation week dayInitial Consultation week dayNaNYesNo2018-06-14 02:00:00+00All Sorted PhysiotherapyNaNNaNNaN1984-09-28FemaleGLADSTONENaNNo2
213NoNaN0013Not Yet ActionedNew To ClinicLate MorningEnd monthThursdayMay24.021.0Not RebookedNaN-25835.632021-05-04 13:07:51+00Initial Consultation week dayInitial Consultation week dayNaNYesNo2018-05-24 01:30:00+00All Sorted PhysiotherapyNaNMsNaN1991-09-19FemaleGLADSTONENaNNo2
313NoNaN0136Not Yet ActionedRecurringLate AfternoonMid monthThursdayJune14.024.0Not RebookedNaN-25327.632021-05-04 13:07:46+00Subsequent Consultation week daySubsequent Consultation week dayNaNYesNo2018-06-14 05:30:00+00All Sorted PhysiotherapyNaNMrNaN1956-10-10MaleGLADSTONENaNNo2
413NoNaN0174Not Yet ActionedRecurringLate MorningEarly monthThursdayJuly5.027.0Not RebookedNaN-24830.132021-05-04 13:07:42+00Subsequent Consultation week daySubsequent Consultation week dayNaNYesNo2018-07-04 23:00:00+00All Sorted PhysiotherapyNaNMsNaN1997-01-16FemaleGLADSTONENaNNo2
516NoNaN000Not Yet ActionedNew To ClinicLate MorningEnd monthFridayApril21.016.0Not RebookedNaN0.00NaN⚡️Osteopathy - Subsequent Consultation with Supervising ClinicianOsteopathy - Subsequent Consultation with Supervising Clinician2. Subsequent Osteopathy Consultations for Existing PatientsNoNo2023-04-20 23:00:00+00⬅️ Front Desk MigrationNaNNaNNaN1958-06-24NaNNaNBaristaNo2
616NoNaN000Not Yet ActionedNew To ClinicLate MorningEnd monthFridayJuly21.029.0Not RebookedNaN0.00NaN⚡️Osteopathy - Subsequent Consultation with Supervising ClinicianOsteopathy - Subsequent Consultation with Supervising Clinician2. Subsequent Osteopathy Consultations for Existing PatientsNoNo2023-07-20 23:30:00+00⬅️ Front Desk MigrationNaNNaNNaN1971-03-12NaNNaNNaNNo2
716NoNaN000Not Yet ActionedNew To ClinicLate MorningEnd monthFridayMarch24.012.0Not RebookedNaN0.00NaN⚡️Osteopathy - Subsequent Consultation with Supervising ClinicianOsteopathy - Subsequent Consultation with Supervising Clinician2. Subsequent Osteopathy Consultations for Existing PatientsNoNo2023-03-23 23:00:00+00⬅️ Front Desk MigrationNaNNaNNaN1994-04-16NaNNaNRetail ManagerNo2
816NoNaN000Not Yet ActionedNew To ClinicLate MorningEnd monthWednesdayFebruary22.08.0Not RebookedNaN0.00NaN⚡️Osteopathy - Subsequent Consultation with Supervising ClinicianOsteopathy - Subsequent Consultation with Supervising Clinician2. Subsequent Osteopathy Consultations for Existing PatientsNoNo2023-02-21 23:00:00+00⬅️ Front Desk MigrationNaNNaNNaN1944-12-16NaNNaNRetiredNo2
916NoNaN000Not Yet ActionedNew To ClinicLate MorningMid monthFridayJuly14.028.0Not RebookedNaN0.00NaN⚡️Osteopathy CDM - Subsequent Consultation with Supervising ClinicianOsteopathy CDM - Subsequent Consultation with Supervising ClinicianCDM (Medicare / GP Referrals)NoNo2023-07-13 23:30:00+00⬅️ Front Desk MigrationNaNNaNNaN1960-01-17NaNNaNCleanerNo2